Insightforge: Forecasting Market Movements Using Machine Learning Algorithms

Abstract

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Forecasting accuracy is the most important factor in selecting any forecasting methods. Research efforts in improving the accuracy of forecasting models have been increasing since the last decade. The appropriate stock selections that are suitable for investment is a very difficult task. The key factor for each investor is to earn maximum profits on their investments.

ecause of dependency on various factors, the stock prices are dynamic, highly noisy, and nonlinear time series data. The stock market prediction has always caught the attention of many analysts and researchers. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this project, we first provide a concise review of stock markets, we then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock.

This project aims to shed light on the process of web scraping, emphasizing its importance in the new ’Big Data’ era with an illustrative application of such methods in financial markets. The work essentially focuses on different scraping methodologies that can be used to obtain large quantities of heterogeneous data in real-time. Automatization of data extraction systems is one of the main objectives pursued in this work, immediately followed by the development of a framework for predictive modeling. applying neural networks and deep learning methods to the data obtained through web scraping.

Anyone with nary an idea on the stock market will have a tool that may assist them in the quicks and that is day trading. The economy of our country is not a toy for huge corporations to fiddle with. It is the livelihood of many and retribution against the corporations is due with the first step being the mob short selling of GameStop by Redditors. These methods are applied on 5 years of data retrieved from Yahoo Finance. The results will be used to analyze and predict price.

DOI: https://www.doi-ds.org/doilink/06.2025-15196855